Fidgeting is a highly common behavior, with excessive fidgeting associated with attention-deficit/hyperactivity disorder (ADHD). Studies from our laboratory (1) and colleagues (2) suggest physical movement can enhance cognitive performance in children with ADHD. Hyper-sensorimotor behavior may be related to impaired regulation of arousal in the noradrenergic and dopaminergic systems (3). This project will assess if frequency and characteristics of sensorimotor behavior relates to cognitive and emotional response in adults with ADHD, in a fine-grained manner, unlike other studies. We will test if intrinsic fidgeting (Aim 1) and access to a specially designed fidget device (Aim 2) modulates behavioral and physiological response in cognitively and emotionally-demanding contexts. The hype of the commercially available Fidget Cube, its competitors and fidget spinners suggest it might, but there is no systematic evidence to inform consumers, a gap, we aim to fill. ADHD is a significant problem in adulthood, with estimates of 4.4% in the population (4). It is associated with higher rates of substance use disorders, traffic accidents and employment challenges and a national annual economic impact ranging from $143 to $266 billion (5). While overt hyperactivity is mostly associated with childhood, subtler, finer-grained frequent movements (e.g., leg movements, doodling, clicking objects, tapping) are highly common in adult ADHD. Little is known about the characteristics of fidgeting in adulthood or whether it can be harnessed to enhance self-regulation with the use of an external device. Our aims are as follows: Aim 1: Assess in a randomized controlled study if a) intrinsic fidgeting and b) use of a smart fidget device improves attention, working memory, processing speed and emotional regulation in adult ADHD; Aim 2: Identify specific touch characteristics associated with cognitive and emotional regulation in adult ADHD using behavioral coding and a prototype fidget ball (developed by Co-I Isbister) with embedded pressure sensors on the fidget surface transmitting real time data to a computer for data analysis; Exploratory Aim 3: Conduct a machine learning analysis of fidgeting behavior in relation to cognitive performance and emotional regulation to: 1) automate recognition of touch features present in fidgeting in adult ADHD; 2) correlate touch sequences with cognitive performance measures and; 3) recommend fidgeting strategies that should prove effective in a given situation. This project will build upon prior work by PI Schweitzer, with her expertise in ADHD, clinical translational research and cognitive neuroscience and Co-I Isbister, with expertise in computer science and engineering, who developed sensor-enabled, smart fidget devices with the goal of improving self-regulation of mood and attention (6-9); Co-I Shapiro (10) with machine learning expertise in analyzing fidgeting behavior. This project is highly responsive to NIMH Strategic Plan Objective 3 as it will inform researchers working to develop new interventions based on behavioral and physiological markers, tailored to the individual.